Large data sets are being used increasingly for everything from predicting consumer behavior to managing traffic conditions on the roadways. Continuing improvements in storage technology mean that many of the previous barriers to managing large data sets are disappearing, allowing even relatively small organizations to store and process large databases. More and more, metadata gleaned from analysis of data is also being generated, used, and stored. But data is only as useful as the ability to retrieve that data. Analyzing and retrieving data and metadata at a reasonable speed is important not just for governments and large research organizations but also for enterprises and even some individual users.
Traditional systems for analyzing and retrieving data may aggregate the data to be analyzed at the time of the query. This aggregation may take a substantial amount of time and reduce the number of searches that may be run in a reasonable time frame. Some traditional systems may attempt to solve this problem by maintaining a table of commonly referenced metadata. However, such mechanisms that track metadata may become unsynced from their source tables due to an accumulation of minor flaws during the update process. Accordingly, the instant disclosure identifies an addresses a need for systems and methods for maintaining aggregate tables in databases.